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Record W2140849196 · doi:10.1139/f06-116

Effect of lipid extraction on the interpretation of fish community trophic relationships determined by stable carbon and nitrogen isotopes

2006· article· en· W2140849196 on OpenAlexvenueno aff
Brent A. Murry, John M. Farrell, Mark A. Teece, Peter M. Smyntek

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTrophic levelFood webδ13CIsotopes of carbonStable isotope ratioExtraction (chemistry)δ15NFish <Actinopterygii>Isotopes of nitrogenCommunity structureIsotope analysisCarbon fibersBiologyChemistryEcologyFisheryMathematicsChromatographyTotal organic carbonPhysics

Abstract

fetched live from OpenAlex

Stable isotopes of carbon (C) and nitrogen (N) are commonly used to evaluate trophic relationships and food web structure; however, the decision to extract lipids or not may influence the interpretation of results. Lipid extraction is not a universal practice, thus pooling or comparing results across studies may not always be appropriate. Additionally, common lipid extraction techniques remove not only lipids, but also N-containing compounds that may alter the δ 15 N value of a sample. We examined differences in the interpretation of fish community trophic structure derived from δ 13 C and δ 15 N stable isotope data based on lipid-extracted and nonextracted samples from nine freshwater fish species. Lipid extraction significantly increased δ 13 C and δ 15 N, causing a positive shift in overall food web placement. The magnitude of isotopic change did not, however, differ among species, such that the overall interpretation of the fish community structure was not altered. The consistent increase in both C and N isotopes did, however, significantly alter the placement of the food web in coordinate space relative to nonextracted webs. Cross-study comparisons need to consider these procedural inconsistencies when drawing conclusions from multiple studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.215
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations103
Published2006
Admission routes1
Has abstractyes

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